{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true, "pycharm": { "name": "#%% md\n" } }, "source": [ "# Tutorial: Generative Adversarial Networks - Advanced Techniques\n", "### - *Jonas Glombitza, RWTH Aachen University, IML 2019, CERN*\n", "This tutorial is about _generative models_ and especially **Generative Adversarial Networks** (**GANs**).\n", "In this tutorial we will implement different types of GANs, which were proposed recently:\n", "- Vanilla GAN - https://arxiv.org/abs/1406.2661\n", "- Conditional GAN - https://arxiv.org/abs/1610.09585\n", "- Wasserstein GAN (WGAN-GP) - https://arxiv.org/abs/1704.00028\n", "- Spectral Normalization SN-GAN - https://arxiv.org/abs/1802.05957\n", "\n", "and learn about further techniques to stabilize the training of GANs. (DCGANs, conditioning of the generator ...).\n", "To train our generative models, we will have a look on three different data sets (1 from computer vision, 2 physics data sets):\n", "1. [ CIFAR10 ](https://www.cs.toronto.edu/~kriz/cifar.html)\n", "2. [ Footprints of Cosmic Ray induced Air Showers ](https://link.springer.com/article/10.1007/s41781-018-0008-x)\n", "3. [ Electromagnetic Calorimeter Images (multi-layer) ](https://doi.org/10.1007/s41781-018-0019-7)\n", "\n", "As framework, we make use of [TensorFlow](https://www.tensorflow.org/) and especially:\n", "- [Keras](https://keras.io/): Keras API shipped with TensorFlow\n", "- [TensorFlow-GAN](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/gan): lightweight library for training GANs\n", "\n", "#### Table of contents\n", "1. [ Basics ](#basics)\n", "\n", "2. [ Generative Adversarial Networks ](#gan)\n", " 1. [ Theory ](#gan_theory)\n", " 2. [ CIFAR10: Example](#gan_code)\n", " 3. [Implementation](#gan_code)\n", " \n", "3. [ Wasserstein GANs ](#wgan)\n", " 1. [ Theory ](#wgan_theory)\n", " 2. [ CIFAR10: Example](#wgan_code)\n", " 3. [ Results](#wgan_result)\n", " \n", "4. [ Spectral Normalization for GANs ](#sngan)\n", " 1. [ Theory ](#sngan_theory)\n", " 2. [ Physics Example: Cosmic Ray induces Air Showers](#sngan_code)\n", " 3. [ Results](#sngan_result)\n", " \n", "5. [ Calorimeter Images](#calgan)\n", " 1. [ Generator conditioning ](#calgan_theory)\n", " 2. [ Physics Example: Calorimeter images](#calgan_code)\n", " 3. [ Results](#calgan_result)\n", " \n", " \n", "\n", "# Basics\n", "  \n", "\n", "## Generative Models\n", "Before we jump into the implementation of several GANs, we need to introduce _Generative Models_.\n", "\n", "Let us assume we have a bunch of images which forms the distribution of real images $P_{r}$.\n", "In our case (CIFAR10 dataset) the distribution consists of several classes: `horse, airplane, bird, frog, truck, deer, cat, dog, car, ship`.\n", "Instead of training a classifier to be able to label our data, we now would like to generate samples which are \n", "similar to samples in the distribution $P_{r}$ formed by the bunch of images.\n", "The clue is, that we would like to generate **new samples** which were **not part** of the dataset, but look really similar.\n", " \n", "![CIFAR 10 images](https://cernbox.cern.ch/index.php/s/xDYiSmbleT3rip4/download?path=%2Fgan%2Fimages&files=CIFAR10_collection.png)\n", "\n", "So in a mathematical way, we would like to approximate the real distribution $P_{r}$ with a model $P_{\\theta}$.\n", "With this *generative* model, we then would like to generate new samples ($x \\sim P_{\\theta}$).\n", "\n", "\n", "## Generative Adversarial Networks\n", "The basic idea of Generative Adversarial Networks (GANs) is to train a **generator network** to learn the underlying data distribution.[1](#myfootnote1)\n", "In other words, we would like to design a generator machine, we can feed with noise and which outputs us nice samples following\n", "the distribution of real images $P_{r}$, but which were not part of the training dataset.\n", "So in our case, we would like to generate new samples of airplanes, cars, dogs etc..\n", " \n", " \"generator_machine\"\n", "\n", "In our setup we use a neural network $G(z)$ as generator machine. \n", "The generator network $G(z)$ gets as input a noise vector $z$ sampled from a multidimensional noise distribution $z \\sim p(z)$.\n", "This space of $z$ is often called latent space. The generator should then map the noise vector $z$ into the data space (the space where our\n", "real data samples lie) and output new samples/images $\\tilde{x} \\sim G(z)$. We would like to have these $\\tilde{x}$ very similar to the real samples $x \\sim P_{r}$.\n", "\n", "For the training of our generator network we need feedback, if the generated samples are of good or bad quality.\n", "Because a classical supervised loss is incapable for giving a good feedback to the generator network, it is trained in an unsupervised manner.\n", "So instead of using \"mean squared error\" or similar metrics, the performance measure is given by a **second** _adversarial_ neural network, which is called _discriminator_.\n", "In the vanilla GAN setup, the way of measuring the quality of the generated samples should be given by a classifier which is trained\n", "to classify between fake `class=0` (generated by our generator network) and real images `class=1` (from our bunch of images).\n", "\n", "The clue is, that the generator should try to fool the discriminator.\n", "So, by adapting the generator weights the discriminator should fail to identify the fake images, and should output `class=1` (real images)\n", "when generated images are input into the discriminator. It is crucial, that we can directly get this feedback when stacking the generator on top\n", "of the discriminator to build our GAN framework. Because both are neural networks, we just can propagate the gradient \n", "through the discriminator to the generator, which can then adapts its weights to generate samples of better quality.\n", "In simple words, the generator should change the weights in a such a way, that the discriminator identifies\n", "the generated samples as real images.\n", "When iteratively adapting the weights of the discriminator and generator, the performance of generated images should increase stepwise.\n", "This is the fascinating idea of _adversarial training_.\n", "\n", "\n", "\"drawing\"\n", "\n", "In a very figurative sense, the discriminator could be seen as painter which is able to classify between\n", "real images and fake images, because he knows a little bit about colors and art. We now would like to build a generator machine\n", "which produce nice photographs.\n", "The idea is, to fool the painter (discriminator) by changing the parameters of our machine.\n", "Because the painter has knowledge how \"real images\" look like, the feedback of him helps us to modify the generator machine in such a way that it will produce images of better quality.\n", "\n", "\n", "### Adversarial training\n", "After introducing the basic idea of adversarial training, let us now understand the math and focus on the algorithm itself.\n", " \n", "Our adversarial framework consists of 2 networks:\n", "- the generator network $G$ (learn the mapping from noise $z$ to images $\\tilde{x}$)\n", "- the discriminator $D$ network (measures the image quality, by discriminating if the images are true `class=1` or fake `class=0`)\n", "\n", "The iterative update procedure of the framework is as follows:\n", " 1. Discriminator update: train discriminator to classify between fake and real images\n", " 2. Generator update: train generator to fool the discriminator\n", " - Repeat from beginning\n", "\n", "\n", "#### Discriminator update\n", "Sample noise vectors from the latent space $z \\sim p(z)$.\n", "Generate new fake samples by feeding the sampled vectors $z$ into the generator $G(z)$ to obtain the new samples $\\tilde{x} \\sim G(z)$.\n", "Subsequently, we sample from the real distribution and obtain a bunch of samples $ x \\sim P_{r}$.\n", "We now train the discriminator using the binary cross entropy by changing the weights $w$ of the discriminator:\n", "\n", "$ \\mathcal{L}_{Dis} = \\min{ -\\mathbb{E}_{\\mathbf{x} \\sim p_{data}(\\mathbf{x})} [log(D_w(\\mathbf{x}))] - \\mathbb{E}_{\\mathbf{z} \\sim p_z(\\mathbf{z})} [log(1-D_w(G_{\\theta}(\\mathbf{z})))]}$\n", "\n", "This is just the typical supervised training of a classifier.\n", "\n", "\n", "#### Generator update\n", "Firs, we sample noise from the latent space $z \\sim p(z)$.\n", "Generate new fake samples by feeding the noise into the generator $G(z)$ and obtain a bunch of generated samples $\\tilde{x} \\sim G(z)$.\n", "Now we freeze the weights $w$ of the discriminator.\n", "Finally, we train the generator to fool the discriminator, by adapting the weights $\\theta$ of our generator network:\n", "\n", "$ \\mathcal{L}_{Gen} = \\max{ -\\mathbb{E}_{\\mathbf{z} \\sim p_z(\\mathbf{z})} [log(1-D_w(G_{\\theta}(\\mathbf{z})))]}$\n", "\n", "By iteratively passing these steps of discriminator and generator updates, we train our framework.\n", "\n", "### Architectures\n", "Building a powerful architecture of the discriminator and generator is rucial for successful GAN training.\n", "Remember, that the generator maps from the latent space into the data space.\n", "Hence, the input should be a 1D-vector of noise variables and the output should have image dimensions.\n", "In this case the output will have the dimension `(32, 32, 3)`. (3 color channels: RGB)\n", "\n", "[Radford, Metz, and Chintala](https://arxiv.org/abs/1511.06434) proposed stable architectures for generator and discriminator networks.\n", "These DCGAN \"guidelines\" can be summarized as follows:\n", "- Replace fully connected layers with convolutional layers\n", "- Do not use pooling layers, use striding instead\n", "- Make use of batch normalization in generator and discriminator to stabilize training[2](#myfootnote2)\n", "- Use [LeakyReLU](https://arxiv.org/pdf/1505.00853.pdf) activation in discriminator for better feedback[3](#myfootnote3)\n", "- Use a pyramidal topology in the generator by using transposed convolutions, to support a simple and structured latent space\n", "\n", "\"drawing\"\n", "
image credit: Radford, Metz, Chintala
\n", "\n", "\n", "\n", "_Footnotes_\n", "\n", "0: Let us assume we are using mean squared error $(\\tilde{x}-x)^2$ as loss. This will lead to 2 problems.\n", "- we learn sample generation in a strange way: we input noise and assign somehow a label (true image) to this noise. In the next epoch we take completly different noise but would assign the same label.\n", "- even if the generated sample has good quality (maybe a nearly perfect cat), the error would be large when the sample is shifted by a few pixel, rotated or somehow transformed with respect to the \"label\" (real image)\n", "\n", "\n", "1: In contrast to e.g. [_Variational Autoencoders_](https://arxiv.org/abs/1312.6114) the idea is not to \"fit the distribution in a space of compressed representation\"\n", "but to train a generator which approximates the real distribution directly.\n", "Remember that in VAEs we learn a mapping in to the latent space where we can \"fit\" a gaussian. Therefore, after the training we can just\n", "generate new samples by sampling from the latent space using the Gaussian prior.\n", "Because most problems are to complex, the Gaussian is not able to capture all modes, this leads to blurry images which is\n", "a well known problem for VAE generated samples. Furthermore, VAEs minimize the Kullback-Leibler divergence, which is not a well suitable distance measure.\n", "\n", "2: Batch normalization is very important here. Due to the normalization of the gradients, it stabilizes the training.\n", "As we will see [later](#sngan_normalization) normalizing the gradients delivered by the discriminator, is crucial.\n", "\n", "3: Using ReLU in the discriminator would lead to sparse gradient (no negative gradient could propagate back).\n", "Using LeakyReLU provides better feedback. Also pooling would provide sparse gradients.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "  \n", "\n", "## Implementation Vanilla GAN\n", "Let us start to implement our first GAN in Keras, to gain more insights of the training procedure.\n", "First we need to import the libaries." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Python version', '2.7.12')\n", "('TensorFlow version', '1.13.1')\n", "('Keras version', '2.2.4-tf')\n" ] } ], "source": [ "import numpy as np\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "from platform import python_version\n", "import tutorial as tut\n", "%matplotlib inline\n", "print(\"Python version\", python_version())\n", "print(\"TensorFlow version\", tf.__version__)\n", "print(\"Keras version\", keras.__version__)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "First of all we have to load our training data.\n", "Furthermore, we can have a look on the first 16 images of the CIFAR10 dataset.\n", "For the later training of networks it is from big advantage to preprocess our data, because neural networks prfoit from \n", "normalize data we rescale the images to [-1,1]." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "ename": "MemoryError", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mMemoryError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcifar10\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplot_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mx_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m255.\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mMemoryError\u001b[0m: " ] } ], "source": [ "from matplotlib import pyplot as plt\n", "from plotting import plot_images\n", "\n", "(images, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n", "plot_images(images[:16])\n", "x_train = 2 * (images / 255. - 0.5)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false } }, "source": [ "Now we can design our generator and discriminator network.\n", "Let us begin with a straight forward implementation of the generator network, following the DCGAN guidelines.\n", "As noise input we sample from a `latent_dim = 64` dimensional latent space." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "pycharm": { "metadata": false, "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generator\n", "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Colocations handled automatically by placer.\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input_1 (InputLayer) (None, 64) 0 \n", "_________________________________________________________________\n", "dense (Dense) (None, 2048) 133120 \n", "_________________________________________________________________\n", "reshape (Reshape) (None, 2, 2, 512) 0 \n", "_________________________________________________________________\n", "conv2d_transpose (Conv2DTran (None, 4, 4, 512) 6554112 \n", "_________________________________________________________________\n", "batch_normalization_v1 (Batc (None, 4, 4, 512) 2048 \n", "_________________________________________________________________\n", "conv2d_transpose_1 (Conv2DTr (None, 8, 8, 256) 3277056 \n", "_________________________________________________________________\n", "batch_normalization_v1_1 (Ba (None, 8, 8, 256) 1024 \n", "_________________________________________________________________\n", "conv2d_transpose_2 (Conv2DTr (None, 16, 16, 128) 819328 \n", "_________________________________________________________________\n", "batch_normalization_v1_2 (Ba (None, 16, 16, 128) 512 \n", "_________________________________________________________________\n", "conv2d_transpose_3 (Conv2DTr (None, 32, 32, 64) 204864 \n", "_________________________________________________________________\n", "batch_normalization_v1_3 (Ba (None, 32, 32, 64) 256 \n", "_________________________________________________________________\n", "conv2d_transpose_4 (Conv2DTr (None, 32, 32, 64) 102464 \n", "_________________________________________________________________\n", "batch_normalization_v1_4 (Ba (None, 32, 32, 64) 256 \n", "_________________________________________________________________\n", "conv2d (Conv2D) (None, 32, 32, 3) 1731 \n", "=================================================================\n", "Total params: 11,096,771\n", "Trainable params: 11,094,723\n", "Non-trainable params: 2,048\n", "_________________________________________________________________\n", "None\n" ] } ], "source": [ "layers = keras.layers\n", "\n", "print('Generator')\n", "latent_dim = 64\n", "generator_input = layers.Input(shape=[latent_dim])\n", "x = layers.Dense(2 * 2 * 512, activation='relu')(generator_input)\n", "x = layers.Reshape([2, 2, 512])(x)\n", "x = layers.Conv2DTranspose(512, (5, 5), strides=(2, 2), padding='same', activation='relu')(x)\n", "x = layers.BatchNormalization()(x)\n", "x = layers.Conv2DTranspose(256, (5, 5), strides=(2, 2), padding='same', activation='relu')(x)\n", "x = layers.BatchNormalization()(x)\n", "x = layers.Conv2DTranspose(128, (5, 5), strides=(2, 2), padding='same', activation='relu')(x)\n", "x = layers.BatchNormalization()(x)\n", "x = layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', activation='relu')(x)\n", "x = layers.BatchNormalization()(x)\n", "x = layers.Conv2DTranspose(64, (5, 5), padding='same', activation='relu')(x)\n", "x = layers.BatchNormalization()(x)\n", "x = layers.Conv2D(3, (3, 3), padding='same', activation='tanh')(x)\n", "generator = keras.models.Model(inputs=generator_input, outputs=x)\n", "print(generator.summary())" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "As we can see, we build a generator with ~ 10 M parameters, which maps from the latent space `(64,)` to the data space `(32,32,3)`.\n", "Notice, that using `tanh` as last activation is important, because we scaled the images to [-1,1].\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Discriminator\n", "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input_2 (InputLayer) (None, 32, 32, 3) 0 \n", "_________________________________________________________________\n", "conv2d_1 (Conv2D) (None, 16, 16, 64) 1792 \n", "_________________________________________________________________\n", "leaky_re_lu (LeakyReLU) (None, 16, 16, 64) 0 \n", "_________________________________________________________________\n", "conv2d_2 (Conv2D) (None, 8, 8, 128) 131200 \n", "_________________________________________________________________\n", "leaky_re_lu_1 (LeakyReLU) (None, 8, 8, 128) 0 \n", "_________________________________________________________________\n", "conv2d_3 (Conv2D) (None, 4, 4, 256) 524544 \n", "_________________________________________________________________\n", "leaky_re_lu_2 (LeakyReLU) (None, 4, 4, 256) 0 \n", "_________________________________________________________________\n", "conv2d_4 (Conv2D) (None, 2, 2, 512) 2097664 \n", "_________________________________________________________________\n", "leaky_re_lu_3 (LeakyReLU) (None, 2, 2, 512) 0 \n", "_________________________________________________________________\n", "flatten (Flatten) (None, 2048) 0 \n", "_________________________________________________________________\n", "dropout (Dropout) (None, 2048) 0 \n", "_________________________________________________________________\n", "dense_1 (Dense) (None, 2) 4098 \n", "=================================================================\n", "Total params: 2,759,298\n", "Trainable params: 2,759,298\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "None\n" ] } ], "source": [ "print('Discriminator')\n", "discriminator_input = layers.Input(shape=[32, 32, 3])\n", "x = layers.Conv2D(64, (3, 3), strides=(2, 2), padding='same')(discriminator_input)\n", "x = layers.LeakyReLU(0.2)(x)\n", "x = layers.Conv2D(128, (4, 4), padding='same', strides=(2, 2))(x)\n", "x = layers.LeakyReLU(0.2)(x)\n", "x = layers.Conv2D(256, (4, 4), padding='same', strides=(2, 2))(x)\n", "x = layers.LeakyReLU(0.2)(x)\n", "x = layers.Conv2D(512, (4, 4), padding='same', strides=(2, 2))(x)\n", "x = layers.LeakyReLU(0.2)(x)\n", "x = layers.Flatten()(x)\n", "x = layers.Dropout(0.25)(x)\n", "x = layers.Dense(2, activation='softmax', kernel_regularizer=keras.regularizers.l1_l2(0.0004))(x)\n", "discriminator = keras.models.Model(inputs=discriminator_input, outputs=x)\n", "print(discriminator.summary())" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%% md\n" } }, "source": [ "Furthermore, we created our discriminator which classifies the input images of shape `(32,32,3)` into `(2,)` classes (fake, real)\n", "holding ~3 M parameters.\n", "\n", "Now, we can compile our model by choosing an optimizer and setting the objective." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [], "source": [ "d_opt = keras.optimizers.Adam(lr=2e-4, beta_1=0.5, decay=0.0005)\n", "discriminator.compile(loss='binary_crossentropy', optimizer=d_opt, metrics=['accuracy'])\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "After creating our discriminator model, we have to build and compile the GAN framework by stacking the discriminator on \n", "top of the generator.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generative Adversarial Network\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input_3 (InputLayer) (None, 64) 0 \n", "_________________________________________________________________\n", "model (Model) (None, 32, 32, 3) 11096771 \n", "_________________________________________________________________\n", "model_1 (Model) (None, 2) 2759298 \n", "=================================================================\n", "Total params: 13,856,069\n", "Trainable params: 13,854,021\n", "Non-trainable params: 2,048\n", "_________________________________________________________________\n", "None\n" ] } ], "source": [ "print('\\nGenerative Adversarial Network')\n", "gan_input = layers.Input(shape=[latent_dim])\n", "gan_output = discriminator(generator(gan_input))\n", "GAN = keras.models.Model(gan_input, gan_output)\n", "print(GAN.summary())\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "After building the framework, we have to compile it.\n", "But before, we have to **freeze the weights of the discriminator**. (Remember that for training the generator, we have to fix the discriminator weights, because we want to fool the discriminator by drawing nice images, not by making our discriminator \n", "a bad classifier)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [], "source": [ "def make_trainable(model, trainable):\n", " \"\"\" Function to freeze / unfreeze a model \"\"\"\n", " model.trainable = trainable\n", " for l in model.layers:\n", " l.trainable = trainable\n", "\n", "g_opt = keras.optimizers.Adam(lr=2e-4, beta_1=0.5, decay=0.0005)\n", "make_trainable(discriminator, False) # freezes the discriminator when training the GAN\n", "GAN.compile(loss='binary_crossentropy', optimizer=g_opt)\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "Note that after we compiled a model, calling `make_trainable` will have no effect until compiling the model again.\n", "\n", "We now can design the training loop of our framework:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [], "source": [ "# loss vector\n", "losses = {\"d\": [], \"g\": []}\n", "discriminator_acc = []\n", "\n", "def train_gan(epochs=0, batch_size=64):\n", " for epoch in range(epochs):\n", " \n", " # Plot some fake images\n", " noise = np.random.randn(batch_size, latent_dim)\n", " generated_images = 255. * (generator.predict(noise) / 2. + 0.5)\n", " plot_images(generated_images[:16]) # plot image\n", " \n", " perm = np.random.choice(50000, size=50000, replace='False')\n", " \n", " for i in range(50000//batch_size):\n", " \n", " # Create a mini-batch of data (X: real images + fake images, y: corresponding class vectors)\n", " image_batch = x_train[perm[i*batch_size:(i+1)*batch_size], :, :, :] # real images\n", " noise_gen = np.random.randn(batch_size, latent_dim)\n", " generated_images = generator.predict(noise_gen) # generated images\n", " X = np.concatenate((image_batch, generated_images))\n", " y = np.zeros([2*batch_size, 2]) # class vector\n", " y[0:batch_size, 1] = 1 # one hote encoding -> real samples\n", " y[batch_size:, 0] = 1 # one hote encoding -> fake samples\n", " \n", " # Train the discriminator on the mini-batch\n", " d_loss, d_acc = discriminator.train_on_batch(X, y)\n", " losses[\"d\"].append(d_loss)\n", " discriminator_acc.append(d_acc)\n", " \n", " # Create a mini-batch of data (X: noise, y: class vectors pretending that these produce real images)\n", " noise_tr = np.random.randn(batch_size, latent_dim)\n", " y2 = np.zeros([batch_size, 2])\n", " y2[:, 1] = 1 # Fooling: We need to take the label 1, (real) because Keras will minimize the cross entropy in our model,\n", " # Switching the label to real here, will let us maximize as required . \n", "\n", " # Train the generator on the mini-batch\n", " g_loss = GAN.train_on_batch(noise_tr, y2)\n", " losses[\"g\"].append(g_loss)\n", " print(discriminator_acc[-1])" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "We now just could train our network by calling `train_gan(epochs=100)`.\n", "\n", "###### WARNING:tensorflow:Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n", "-> This warning is harmless and just occurs, because we compiled the discriminator and later froze its weights for the generator training." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "pycharm": { "is_executing": true, "metadata": false, "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "gan_dir = tut.get_file(\"gan/models/vanilla_GAN/generator.h5\")\n", "pretrained_generator = keras.models.load_model(gan_dir)\n", "noise = np.random.randn(16, 64)\n", "generated_images = 255. * (pretrained_generator.predict(noise) / 2. + 0.5)\n", "plot_images(generated_images) # plot images\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "## Results\n", "\n", "As we can see, the results are very noisy and the generator seems to output really similar image samples of relatively bad quality.\n", "In order not to judge too quickly, you should remember that generating images is a quiet complicated task. 32x32x3 = 3072 dimensional!\n", "The dataset holds 10 classes, with complicated structures. [MNIST](http://yann.lecun.com/exdb/mnist/) is comparatively much much easier.\n", "By randomly sampling from noise, you will never be able to generate a sample looking like any of the images, it will just be noise!\n", "\n", "Still, we can see that our model is only able to produce rough details and local regions of smooth colors, capturing only a single mode.\n", "\n", "\n", "Samples during training | Loss\n", ":-------------------------:|:-------------------------:\n", " | \n", "\n", "\n", "By looking on the generated images during the training, we can see that each iteration another mode (every image look very similar) is displayed.\n", "The generator seems to switch these modes but without improving the general image quality. Our can suffers from mode collapsing!\n", "\n", "When we have a look on the loss we have no visible of convergence or anything which could tell us about successful training.\n", "We can just see that discriminator an generator are on a same scale, but there is no kind of convergence or anything else.\n", "\n", "\n", "For increasing the generation performance we need to understand what's wrong with the vanilla GAN training.\n", "\n", "### Problems of Training GANs\n", " \n", "\n", "\n", "In general, training GANs is a very challenging task and needs a lot of fine tuning compared to supervised training of deep networks.\n", "The reason for this is, that we do not have a stationary minimization problem anymore, but two networks playing a _minmax game_.\n", "The updates of the generator will highly affect the next discriminator update and the other way round.\n", "Because of the non-stationary minimization problem, do not use optimizers with high momentum rates!\n", "\n", "Beside this:\n", "- Mode collapsing\n", "- Vanishing gradients\n", "- Meaningless loss metric\n", "\n", "are further issues of our vanilla GAN framework.\n", "\n", "##### Mode Collapsing - \"Helvetica Scenario\"\n", "After the training, the generator outputs samples from a restricted phase space only.\n", "This is heavily connected to the control of the discriminator gradients. When the discriminator covers only a part of the modes in data,\n", "the feedback will only be informative towards this modes. This heavily drives the generator to collapse towards this mode, which forces the discriminator to focus on a different mode.\n", "\n", "\"drawing\"\n", "\n", "To solve this, one can try to use:\n", "- Batch history (add in the present batch of generated samples, samples generated by the model in earlier iterations)\n", "- Add noise into the framework\n", "- Label smoothing or switching (see [non saturating GAN](#ns_gan))\n", "- Conditioning of GANs - change to semi supervised training by adding label information to generator and/or discriminator\n", " - C-GAN : condition the generator to produce specific samples, [Mirza, Osindero](https://arxiv.org/abs/1411.1784)\n", " - AC-GAN: let the discriminator learn a conditional probability over the classes, [Odena, Olah, Shlens](https://arxiv.org/abs/1610.09585)\n", " \n", "\n", "##### Vanishing Gradients\n", "For training a reasonable generator, the feedback of the discriminator is crucial.\n", "Therefore, one could think that training the discriminator more often than the generator could improve the training,\n", "because the feedback would be much more precise.\n", "The problem is, that for this scenario the feedback of the discriminator is almost zero.\n", "![vanishing gradients](https://cernbox.cern.ch/index.php/s/xDYiSmbleT3rip4/download?path=%2Fgan%2Fimages&files=vanishing_gradients.gif)\n", "
image credit: Emanuele Sansone
\n", "When calculating the gradient for the generator update provided by a converged discriminator, one obtains vanishing feedback:\n", " \n", "$\\lim_{D \\rightarrow 0} \\frac{\\partial{}}{\\partial{\\theta}} \\log(1-D(G_{\\theta}(z))) = \\lim_{f \\rightarrow -\\infty} \\frac{\\partial{}}{\\partial{\\theta}}\\log\\left( 1- \\frac{1}{1+e^{-f_{\\theta}}} \\right) \\approx 0$\n", "\n", "Hence, having a strong discriminator will hinder the training, this is very bad!\n", "In order to prevent that, we used regularization (dropout + l1 + l2) in the discriminator.\n", "\n", "#### Meaningless Loss\n", "By inspecting the loss of the discriminator and generator, we can see that they do not correlate with the image quality.\n", "(the discriminator is not trained to convergence). Hence, it is quiet hard to say if our framework reached a stable equilibrium.\n", "This is really bad for monitoring the training.\n", "\n", "To sum it up, most basic methods which shoud help stabilizing the training process, include noise or noisy updates.\n", "This is terrible, because it will highly affect our performance and will make our generated samples noisy.\n", "\n", "### Towards Softer Metrics\n", "To understand the instable GAN training, one can try to interpret the training of GAN as _divergence minimization_ between the probability densities $P_r$ and $P_\\theta$.\n", "Assuming a optimal discriminator, the traditional GAN setup minimizes the Jensen-Shannon (JS) divergence.\n", "\n", "$\\mathcal{D}_{JS}(P_r|| P_\\theta)=\\mathcal{D}_{KL}(P_r|| P_m) + \\mathcal{D}_{KL}(P_\\theta|| P_m)$, \n", "\n", "with $\\mathcal{D}_{KL}(P_r || P_\\theta )= \\mathbb{E}_{\\mathbf{x} \\sim P_r} log\\left(\\frac{P_r}{P_\\theta}\\right)\\;$ and $P_m = \\frac{1}{2}(P_r + P_\\theta)$.\n", "\n", "The problem of the JS divergence $\\mathcal{D}_{JS}$ is the situation of disjoint distributions. When $P_r \\neq 0$ but $P_\\theta = 0$, the JS divergence is *always* constant $\\mathcal{D}_{JS}=\\log(2)$.\n", "This are very bad news, because especially at the beginning of the training the distributions will be disjoint (the discriminator will perfectly separate fake and real samples), hence it would be important that the discriminator gives a measure how \"close\" $P_r$ and $P_\\theta$ are. (Just think of samples with nice quality, which lie close to $P_r$ and samples which are pure noise, their loss would still be exactly the same).\n", "\n", "This motivates the usage of a much softer metric, which incorparate a notion of distance.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "      \n", "\n", "# Wasserstein GANs\n", "  \n", "\n", "To overcome the issue of the meaningless loss and vanishing gradients,\n", "[Arjovsky, Chintala and Bottou](https://arxiv.org/abs/1701.07875) proposed to used the Wasserstein-1 as metric in the discriminator.\n", "Using the Wasserstein distance as metric has several advantages in comparison to the old minmax loss.\n", "The crucial feature of the Wasserstein distance is the meaningful distance measure even when distributions are disjunct.\n", "But before coming to the crucial difference, let us try to understand the Wasserstein distance.\n", "\n", "### Wasserstein or Earth Mover's distance\n", "Wasserstein-1 or _earth mover's distance_ describes the **minimal cost** to transform a distribution $P_{\\theta}$ into another disitribution $P_{r}$ and vice versa.\n", "The mathematical defintion is as follows:\n", "\n", "$\\mathcal{D}_W(P_r|| P_\\theta)= \\inf_{\\gamma \\in \\Pi(P_r, P_\\theta)} \\mathbb{E}_{(x,y) \\sim \\gamma}[||x-y||]$\n", "\n", "The Wasserstein distance can be understood using a very figurative example.\n", "Let us assume, we have two heaps of earth in the garden $P_{r}, P_{\\theta}$ and would like to know, what is the minimum ${\\inf}$ *work* we need to transform one heap of earth into the other one.\n", "In other words, how many shovels of earth $\\mathbb{E}_{(x,y)}$ we have to transport which distance $||x-y||$ (work: mass * distance).\n", "The optimal transport plan $\\inf_{\\gamma \\in \\Pi(P_r, P_\\theta)}$ for this transformation, is given by the Wasserstein distance. \n", "\n", "\"drawing\"\n", "\n", "\n", "### Improved training of GANs\n", "When interpreting the training of GANs as divergence minimization, the training of vanilla GANs is similar (assuming an optimal discriminator) to minimize the *Jensen-Shannon divergence* (JS divergence).\n", "The JS divergence do not provided a meaningful measure for disjoint distributions (no overlap).\n", "In contrast, the Wasserstein distance does (in the figurative example both heaps of earth are disjoint).\n", "\n", "Because of the easy formulation of the distance measure (mass * distance) we can expect smooth gradients, we are using a much softer metric now. \n", "\n", "### Approximation of the Wasserstein distance using the discriminator (critic)\n", "Using the Kantorovich-Rubinstein duality we are able to construct the Wasserstein distance ourselves and the loss becomes:\n", "\n", "$\\mathcal{L}_{WGAN} = \\min_{ D_w \\in Lip_1} \\mathbb{E}_{x \\sim P_r}[D_w(x)] - \\mathbb{E}_{\\tilde{x} \\sim P_\\theta}{[D_w(\\tilde{x})]}$ ,\n", "\n", "where $D_w(x)$ is the discriminator network (in the WGAN setup called *critic* because the network is not trained to discriminate anymore).\n", "Basically the distance/loss $\\mathcal{L}_{WGAN}$ is formed, by subtracting the output of the critic applied on the fake samples from the critic applied on the real samples.\n", "It is important, that $D \\in Lip_1$ denotes, that the function the critic network should approximate is a 1-Lipschitz function.\n", "(A 1-Lipschitz function is a function, which has a slope $m \\leq 1$ everywhere.)\n", "Therefore, the critic network we used for approximation has to carry this Lipschitz constrain[4](#myfootnote4) also.\n", "For implementing Wasserstein GANs, the are 2 possible methods to enforce the Lipschitz constrain:\n", "- Use weight clipping, which is less preferred[5](#myfootnote4)\n", "- Use gradient penalty\n", "\n", "[Gulrajani et. al.](https://arxiv.org/abs/1704.00028) proposed the **gradient penalty** to construct the Lipschitz constrain by extending the loss $\\mathcal{L}_{WGAN}$ with:\n", "\n", "$\\mathcal{L}_{GP} = \\lambda\\; \\mathbb{E}_{\\hat{u} \\sim P_{\\hat{u}}}[(||\\nabla_{\\hat{u}}D_w(\\hat{u})||_2-1)^2]$\n", "\n", "where $\\lambda$ is a hyperparameter to scale the loss and $\\hat{u}=\\epsilon x + (1-\\epsilon)\\tilde{x}\\;,0 \\leq \\epsilon \\leq 1$\n", "is randomly sampled along straight lines between pairs of data and fake samples.\n", "In simple words, the gradient in the discriminator is forced to have norm $1$[6](#myfootnote6) everywhere between the distribution of real and fake samples.\n", "Implementing this \"objective\" makes explicitly clear, that the WGAN will not suffer from vanishing gradients.\n", "Because we have a non-stationary minimization problem we have to estimate the Wasserstein distance after each generator iteration again. Using more iterations of the critic before the generator update, allows for a more precise estimation of the Wasserstein distance.\n", "\n", "This are great news, as we now have a soft metric which will us provide very nice feedback.\n", "We now can and **should train the critic network to convergence**, which will give us **non vanishing \n", "and precise** feedback.\n", "\n", "\n", "\n", "_Footnotes_\n", "\n", "4: Easy interpretation: We need a constraint to train the discriminator to convergence, otherwise\n", " the discriminator could focus on one feature which differs between real and fake samples and won't converge (which is a necessary requirement for a distance measure)\n", "\n", "5: Weight clamping will heavily reduce the capacity of the discriminator which is unfavourable.\n", "\n", "6: One could argue that by constraining the critic network to provide gradients with exactly norm $1$\n", "is not exactly the same as required by the Lipschitz constraint, which is right sided only $m \\leq 1$. It turns out that\n", "the both-sided penalty, performs slightly better than the right sided gradient penalty. To understand this issue, one have to understand\n", "GAN training more in a way of gradient [control/normalization](#sngan_normalization).\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Implementation of improved Wasserstein GANs - WGAN-GP (conditioned)\n", "After the short introduction of WGANs, let us try to implement our first own Wasserstein GAN using tf.contrib.GAN.\n", "This high-level library will make our life much easier.\n", "\n", "Furthermore, we try to condition our generator to produce samples from a specific class (eg. dog), following the [C-GAN approach](https://arxiv.org/abs/1411.1784)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", "For more information, please see:\n", " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", " * https://github.com/tensorflow/addons\n", "If you depend on functionality not listed there, please file an issue.\n", "\n", "('TensorFlow version', '1.13.1')\n" ] } ], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "from plotting import plot_images\n", "import tutorial as tut\n", "\n", "\n", "layers = tf.layers\n", "tfgan = tf.contrib.gan\n", "print(\"TensorFlow version\", tf.__version__)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "Let us begin to build our data pipeline, which will provide us with new data samples during the training.\n", "First, we need to define our data generator.\n", "The data generator should output real samples (input for the discriminator) and noise (input for the generator).\n", "The variable LATENT_DIM defines the dimensionality of the latent space of the generator. (The noise distribution we sample from).\n", "\n", "Additionally, we will provide our generator and discriminator a label, to condition and stabilize our training process.\n", "These type of GANs are called [Conditional GANs](https://arxiv.org/abs/1610.09585) or supervised." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [], "source": [ "def data_generator(LATENT_DIM):\n", " (images, labels), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n", " nsamples = images.shape[0]\n", " images = 2 * (images / 255. - 0.5)\n", " while True:\n", " idx = np.random.permutation(nsamples)\n", " for i in idx:\n", " yield ((np.random.randn(LATENT_DIM), labels[i]), (images[i].astype(np.float32)))" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false } }, "source": [ "Let us now check if our generator is working, by plotting some real images." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import itertools\n", "generator_output = np.array(list(itertools.islice(data_generator(64), 16)))\n", "test_images = 255. * (generator_output[:,1] / 2. + 0.5)\n", "plot_images(test_images[:16])\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "For training GANs we need to further define our generator and discriminator network.\n", "We start by defining our generator network, which should map from our noise + label space into the space of out images \n", "`(LATENT_DIM + LABEL --> IMAGE_DIM)`.\n", "Adding the label the input of to both the generator and discriminator, should enforce the generator to produce samples from the according class." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [], "source": [ "LATENT_DIM = 64 # dimension of the latent space\n", "\n", "def generator_fn(inputs, latent_dim=LATENT_DIM):\n", " x, labels = inputs\n", " x = tf.concat([x, labels], axis=-1)\n", " x = layers.Dense(4 * 4 * 256, activation='relu', input_shape=(latent_dim + 1,))(x) #\n", " x = tf.reshape(x, shape=[BATCH_SIZE, 4, 4, 256])\n", " x = layers.Conv2DTranspose(256, (4, 4), strides=(2, 2), padding='same', activation='relu')(x)\n", " x = layers.BatchNormalization()(x)\n", " x = layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding='same', activation='relu')(x)\n", " x = layers.BatchNormalization()(x)\n", " x = layers.Conv2DTranspose(64, (4, 4), strides=(2, 2), padding='same', activation='relu')(x)\n", " x = layers.BatchNormalization()(x)\n", " x = layers.Conv2D(3, (3, 3), padding='same', activation='tanh')(x)\n", " return x" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false } }, "source": [ "After defining our generator network we need to implement our discriminator.\n", "The task of the discriminator is to measure the similarity between the fake images (output of the generator) and the real images.\n", "So, the network maps from the image space into a 1D space where we can measure the 'distance' between the distributions \n", "of the real and generated images `(IMAGE_DIM + LABEL --> 1)`.\n", "Also here we add the class label to the discriminator. To insert the information of the class label already in the block of convolutions, we just build a feature map holding the label at each pixel, and stack it on the resulting feature maps of the first convolution." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [], "source": [ "def discriminator_fn(x, gen_in):\n", " \"\"\" Discriminator network \"\"\"\n", " noise, labels = gen_in\n", " x = layers.Conv2D(64, (3, 3), padding='same', input_shape=(32, 32, 3))(x)\n", " x = tf.nn.leaky_relu(x, 0.2)\n", " labels = tf.tile(tf.reshape(labels, [BATCH_SIZE, 1, 1, 1]), [1, 32, 32, 1])\n", " x = tf.concat([x, labels], axis=-1)\n", " x = tf.contrib.layers.layer_norm(x)\n", " x = layers.Conv2D(64, (4, 4), padding='same', strides=(2, 2))(x)\n", " x = tf.contrib.layers.layer_norm(x)\n", " x = tf.nn.leaky_relu(x, 0.2)\n", " x = layers.Conv2D(128, (3, 3), padding='same')(x)\n", " x = tf.contrib.layers.layer_norm(x)\n", " x = tf.nn.leaky_relu(x, 0.2)\n", " x = layers.Conv2D(128, (4, 4), padding='same', strides=(2, 2))(x)\n", " x = tf.contrib.layers.layer_norm(x)\n", " x = tf.nn.leaky_relu(x, 0.2)\n", " x = layers.Conv2D(256, (3, 3), padding='same')(x)\n", " x = tf.contrib.layers.layer_norm(x)\n", " x = tf.nn.leaky_relu(x, 0.2)\n", " x = layers.Conv2D(256, (4, 4), padding='same', strides=(2, 2))(x)\n", " x = tf.contrib.layers.layer_norm(x)\n", " x = tf.nn.leaky_relu(x, 0.2)\n", " x = layers.Conv2D(512, (3, 3), padding='same')(x)\n", " x = tf.nn.leaky_relu(x, 0.2)\n", " x = layers.Flatten()(x)\n", " x = layers.Dense(1)(x)\n", " return x" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "\n", "Note that in WGANs we can not make use of batch normalization in the critic, because the gradient penalty is calculated between sample pairs.\n", "Therefore, [layer normalization](https://arxiv.org/abs/1607.06450) is a great alternative for normalization in the critic.\n", "\n", "So let us use the [gradient penalty](https://arxiv.org/abs/1704.00028):\n", "By penalizing the gradient to be 1, we enforce the lipschitz constraint needed to construct Wasserstein using the\n", "[Kantorovich-Rubinstein duality](https://cedricvillani.org/wp-content/uploads/2012/08/preprint-1.pdf).\n", "\n", "GP is the hyperparameter to weight the strength of the Lipschitz constrain. (higher GP --> stronger enforcing)\n", "Here, we can make use of the preliminary implemented `wasserstein_loss` which evaluates $\\mathcal{L}_{WGAN}$, **without** enforcing a Lipschitz constrain.\n", "For this reason, we have to add the gradient penalty to the loss.\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [], "source": [ "def discriminator_loss(model, add_summaries=True):\n", "\n", " loss = tf.contrib.gan.losses.wasserstein_discriminator_loss(model, add_summaries=add_summaries)\n", " gp_loss = GP * tf.contrib.gan.losses.wasserstein_gradient_penalty(model, epsilon=1e-10, one_sided=True, add_summaries=add_summaries)\n", " loss += gp_loss\n", "\n", " if add_summaries:\n", " tf.summary.scalar('discriminator_loss', loss) # add loss to TensorBoard summary\n", "\n", " return loss" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "After defining our objective, we can choose our training parameters" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [], "source": [ "BATCH_SIZE = 32 # number of samples fed into the framework in each iteration\n", "GEN_LR = 0.0002 # learning rate of the generator\n", "DIS_LR = 0.0002 # learning rate of the discriminator\n", "GP = 10 # factor to scale the gradient penalty (higher means larger enforcing the Lipschitz constrain)\n", "N_CRIT = 5 # number of critic iterations per generator iterations." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "Now we can very easily implement our framework as estimator using tf.gan, this will heavily simplify our training procedure.\n", "It is important to realize that we now train the critic `N_CRIT = 5` times, before updating the generator `1` time.\n", "This allows us to stabilize the training procedure, by precisely approximating the Wasserstein distance." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Using config: {'_save_checkpoints_secs': None, '_num_ps_replicas': 0, '_keep_checkpoint_max': 1, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': , '_model_dir': '/home/jonas/PycharmProjects/iml2019/data/gan/models/WGAN_GP', '_protocol': None, '_save_checkpoints_steps': 200, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_session_config': allow_soft_placement: true\n", "graph_options {\n", " rewrite_options {\n", " meta_optimizer_iterations: ONE\n", " }\n", "}\n", ", '_tf_random_seed': None, '_save_summary_steps': 10, '_device_fn': None, '_experimental_distribute': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_evaluation_master': '', '_eval_distribute': None, '_train_distribute': None, '_master': ''}\n" ] } ], "source": [ "wgan_dir = tut.get_file(\"gan/models/WGAN_GP\", is_dir=True)\n", "\n", "gan_estimator = tfgan.estimator.GANEstimator(\n", " wgan_dir,\n", " generator_fn=generator_fn,\n", " discriminator_fn=discriminator_fn,\n", " generator_loss_fn=tfgan.losses.wasserstein_generator_loss,\n", " discriminator_loss_fn=discriminator_loss,\n", " generator_optimizer=tf.train.AdamOptimizer(GEN_LR, 0.5),\n", " discriminator_optimizer=tf.train.AdamOptimizer(DIS_LR, 0.5),\n", " get_hooks_fn=tfgan.get_sequential_train_hooks(tfgan.GANTrainSteps(1, N_CRIT)),\n", " config=tf.estimator.RunConfig(save_summary_steps=10, keep_checkpoint_max=1, save_checkpoints_steps=200),\n", " use_loss_summaries=True)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false } }, "source": [ "To train our estimator we can create a TensorflowDataset out of our data generator and design a function which outputs us\n", "batches of our dataset.\n", "This batches we can then use to train our estimator." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "pycharm": { "metadata": false, "name": "#%%\n" } }, "outputs": [], "source": [ "def batched_dataset(BATCH_SIZE, LATENT_DIM, data_generator):\n", " Dataset = tf.data.Dataset.from_generator(\n", " lambda: data_generator(LATENT_DIM), output_types=((tf.float32, tf.float32), (tf.float32)),\n", " output_shapes=((tf.TensorShape((LATENT_DIM,)), tf.TensorShape((1,))), (tf.TensorShape((32, 32, 3)))))\n", " return Dataset.batch(BATCH_SIZE)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false } }, "source": [ "Finally, let us train our framework using our gan_estimator and data_pipeline.\n", "\n", "*We skip the training here and import a pretrained estimator*.\n", "To train the estimator, change max_steps to large values or use `steps` instead." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Skipping training since max_steps has already saved.\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gan_estimator.train(lambda: batched_dataset(BATCH_SIZE, LATENT_DIM, data_generator), max_steps=1000)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "\n", "To test the conditioning of the labels we can just build a sorted generator, generate samples and plot the resulting images.\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "pycharm": { "metadata": false, "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/data/ops/dataset_ops.py:429: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "tf.py_func is deprecated in TF V2. Instead, use\n", " tf.py_function, which takes a python function which manipulates tf eager\n", " tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n", " an ndarray (just call tensor.numpy()) but having access to eager tensors\n", " means `tf.py_function`s can use accelerators such as GPUs as well as\n", " being differentiable using a gradient tape.\n", " \n", "INFO:tensorflow:Calling model_fn.\n", "INFO:tensorflow:Done calling model_fn.\n", "INFO:tensorflow:Graph was finalized.\n", "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use standard file APIs to check for files with this prefix.\n", "INFO:tensorflow:Restoring parameters from /home/jonas/PycharmProjects/iml2019/data/gan/models/WGAN_GP/model.ckpt-100000\n", "INFO:tensorflow:Running local_init_op.\n", "INFO:tensorflow:Done running local_init_op.\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from plotting import plot_cond_images\n", "\n", "def sorted_generator(LATENT_DIM):\n", " (images, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n", " labels = y_train\n", " nsamples = images.shape[0]\n", " images = 2 * (images / 255. - 0.5)\n", " while True:\n", " labels = np.expand_dims(np.tile(np.arange(10), 5000), axis=-1)\n", " for i in range(nsamples):\n", " yield ((np.random.randn(LATENT_DIM), labels[i]), (images[i].astype(np.float32) ))\n", "\n", "result = gan_estimator.predict(lambda: batched_dataset(BATCH_SIZE, LATENT_DIM, sorted_generator))\n", "images = []\n", "for i, image in zip(range(30), result):\n", " images.append(255 * (image / 2. + 0.5))\n", "plot_cond_images(images)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "\n", "\n", "### Results\n", "We now can have a look on the improvement of generated samples during the training and inspect the losses of the WGAN. The training took around 12h on a Nvidia 1080 GTX and was trained for 100k generator iterations.\n", "\n", "As we can see, the _gradient penalty_ is converging towards zero! This are great news, that means our critic is able to give a good estimate of the Wasserstein distance.\n", "\n", "##### Gradient Penalty Loss \n", "\"Gradient\n", "\n", "If we look at the _critic loss_ we can see a convergence towards 0. (Note that due to our setup, we are maxmizing the negative Wasserstein distance, the critic gives a negative estimate of Wasserstein-1).\n", "These are really great news, because now we can very easily monitor the GAN training.\n", "In this case, we can see an convergence of the Wasserstein metric and expect good results.\n", "\n", "##### Critic Loss\n", "\"critic\n", "\n", "So let's have a look on the samples during the training:\n", "\n", "\n", "![WGAN_GP samples](https://cernbox.cern.ch/index.php/s/xDYiSmbleT3rip4/download?path=%2Fgan%2Fmodels%2FWGAN_GP&files=cond_WGAN.gif)\n", "\n", "These samples look much better, than the ones obtained using the vanilla approach[6](#myfootnote6).\n", "Even for this relatively simple generator and discriminator model, we can clearly identify in the generated samples features belonging to the according class labels.\n", "Congratulations, we succesfully trained our first Wasserstein GAN!\n", "\n", " \n", " \n", "\n", "_Footnotes_\n", "\n", "7: One could complain that the conditioning on the labels improved the performance and prevent mode collapsing. But indeed, the WGAN-GP setting also performs well when trained completely unsupervised, which is not true for the vanilla GAN setting." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " \n", "  \n", "\n", "# Spectral Normalization for Generative Adversarial Networks - SN-GANs\n", "  \n", "\n", "\n", "\n", "### Gradient Penalty in Terms of Normalization \n", "To understand why SN-GANs are working, one have to interpret the gradient penalty in a little bit different way.\n", "The *gradient penalty* was introduced to estimate the Wasserstein distance between the distribution\n", "of real and fake samples using the _critic_ network. That was desirable when interpreting GAN training in terms of divergence minimization,\n", "because the Wasserstein distance is a much more suitable distance measure for disjoint distributions than the JS-divergence.\n", "Another way to review the gradient penalty is in a way somehow to normalization/ regularization.\n", "Remember, that for training vanilla GANs, using regularization and normalization (noise, regularizer, batch normalization) was crucial\n", "for obtaining reasonable results. But the sort of normalization we focus on in this chapter, is way different.\n", "\n", "\n", "To overcome the issue of *vanishing gradients* already in [Goodfellow et al.](https://arxiv.org/abs/1406.2661) an alternative\n", "loss was proposed, which is known as _label switching_ (because for implementation you just have to switch the labels of true and fakes).\n", "\n", "$ \\mathcal{L}_{NS, Gen} = -\\mathbb{E}_{\\mathbf{z} \\sim p_z(\\mathbf{z})} [log(D(G_{\\theta}(\\mathbf{z})))]$\n", "\n", "The gradients of the discriminator do not vanish anymore, but can provide very instable updates. [Arjovsky and Bottou](https://arxiv.org/abs/1701.04862)\n", "showed, that the provided gradients for a noisy discriminator follows a Cauchy distribution (infinite expectation and variance).\n", "In a more figurative sense, samples which lie outside of the support of the real and fake images will be very noisy and can blow\n", "up massively which disturbs the training.\n", "Therefore, penalizing large gradients in the discriminator is from major importance.\n", "\n", "\n", "These considerations explain the observation, that GANs using the \"non saturating loss\" and the gradient penalty instead of _Wasserstein_ + GP\n", "are an alternative and stable GAN setup. Here, the gradient penalty just penalize large gradients by requiring gradients of norm 1 from the discriminator what also will prevent mode dropping.\n", "\n", "\n", "### Spectral Normalization\n", "\n", "The observation that stabilizing the gradient is a suitable way to train NSGANs (non saturating GANs), leads\n", "to idea to implement the Lipschitz constrain in a much more direct way. (Remember, we have to update the critic several times\n", "to get good feedback for ther generator).\n", "[Miyato, Kataoka, Koyama, Yoshida](https://arxiv.org/abs/1802.05957) proposed to do this, by normalizing the discriminator \n", "weights using the spectral norm.\n", "This will have the big advantage that the discriminator do not need to be updated several times, which increase the speed \n", "of thw GAN training.\n", "\n", "#### Spectral norm\n", "\n", "\n", "Let us remember the definition of the spectral norm:\n", "\n", "$\\sigma(\\mathbf{W}) = ||\\mathbf{W}||_{2} = \\max_{x\\neq 0} \\frac{||\\mathbf{W}x||_2}{||x||_2} = \\max_{||x||_2=1} ||\\mathbf{W}x||_2$.\n", "\n", "Figuratively speaking, the spectral norm of $W$ is the maximum stretch factor of a unit vector $x$ after multiplication with matrix $W$.\n", "\n", "\"drawing\n", "
image credit: Quartl, wikipedia
\n", "\n", "This allows us to draw a connection between the Lipschitz norm and spectral normalization.\n", "\n", "So we can express the Lipschitz norm using the spectral norm:\n", "\n", "$||D(x)||_{\\mathrm{Lip}} = \\sup_{x} \\sigma(\\nabla_x D(x))= \\sup_{x} \\sigma(\\nabla_x Wx) = \\sigma(W)$\n", " \n", "We cann see directly, that for enforcing a similar Lipschitz constrain as used in WGAN-GP $||D(x)||_{\\mathrm{Lip}} \\leq 1$\n", "we just need to normalize the weights $W$ using: \n", "\n", "$\\mathbf{W}_{SN}=\\frac{\\mathbf{W}}{\\sigma(\\mathbf{W})}$.\n", "\n", "\n", "Because calculating the spectral norm can be quiet time consuming, the proposed approach is to use a fast approximation of the spectral norm\n", "using the _power iteration method_.\n", "\n", "Note that this type of normalization is different then typical regularization like L1 or L2, because using spectral\n", "normalization we are **not** adding an additional penalty term to the objective.\n", "\n", "In a figurative sense, just think of the normalization of the SN-GAN, as referee to control the gradient.\n", "Our discriminator now get as as input a vector $f$ of features. In the first layer the weight matrix $W$ is multiplied with this vector $f$.\n", "(normal transformation in fully connected layers). So the feature vector is rotated and stretched.\n", "Now, we constrain the weight matrix $W$ with the spectral norm. Hence, no transformations with a stretching factor larger than 1\n", "is allowed (just see the image of the spectral norm for an easy understanding). These normalization helps to prevent that\n", "the layer focus on a specific feature (direction) only which would induce large gradients.\n", "This helps to control the gradients of the discriminator outside the distribution of real and fake samples and prevents mode collapsing.\n", "\n", "\n", "### Comment on Mode Dropping\n", "One now could argue that spectral normalization will not be able to prevent GANs from mode collapsing, because we are not minizing a soft metric like the Wasserstein distance.\n", "\n", "To understand why SNGANs do not suffer from mode collapsing, one could interpret mode collapsing as overtraining of the dscriminator. The discriminator can very easily find features which are different between real and fake images (we are dealing with very high dimensional data). Very likely, the discriminator will focus on one or a few features very particular.\n", "Therefore, the gradients of the discriminator will all point towards a very restricted space of the phase space and become very large.\n", "This will heavily push the generator, which will collapse at the restricted space of the phase space. Subsequently, the discriminator will focus onto another part of the phase space, the generator do not cover. This will shift the generator to the next mode. This leads to the cycling behavior we observed by training the vanilla GAN in the beginning on CIFAR10.\n", "\n", "By normalizing the gradients, we are somehow regularizing the feedback of our disriminator. Hence, the generator can not be pushed towards a single mode at once. This make mode collapsing very unlikely.\n", "\n", "\n", "\n", "\n", "\n", "## Spectral Normalization GANs for Cosmic Ray induced Air Showers\n", "\n", "After dealing with the CIFAR dataset let us now tackle our first **physics dataset** with GANs:\n", "a dataset of cosmic ray induced air showers.\n", "When ultra-high energy cosmic rays (UHECRs) penetrate the atmosphere of the earth, they interact with the air molecules and \n", "particle cascades are created. For air showers induced by UHECRs (energies > 10 EeV) the footprint on the Earth's surface\n", "is in the order of several 10 km².\n", "The dataset contains footprints of air showers measured by observatories like the [Pierre Auger Observatory](https://www.auger.org/)\n", "and induced by cosmic rays between 1 - 100 EeV and was used to train the first Wasserstein GAN on physics data [Comput Softw Big Sci (2018) 2: 4.](https://link.springer.com/article/10.1007%2Fs41781-018-0008-x).\n", "The dataset was generated using a parametrized simulation, and described in [Astropart. Phys. 97 (2017) 46](https://www.sciencedirect.com/science/article/pii/S0927650517302219?via%3Dihub),\n", "additionally [you can download the code](https://git.rwth-aachen.de/DavidWalz/airshower).\n", "\n", "Note that several things changed, after switching the dataset from photographs to physics:\n", "\n", "|Type| Signal Patterns (Calorimeter images) | Pictures |\n", "| :------------- :| :------------- :|:-------------:|\n", "| Channels | many, up to tenth of layers | 3 (RGB) |\n", "| Resolution | relatively low (20 x 20) | high resolution (higher than 1000 x 1000) |\n", "| Pixel range | several magnitudes, **exponential** | 0-255, **~gaussian**|\n", "| Distribution of pixels | sparse, high local correlations | coherent, high local and global correlations |\n", "\n", "Especially the logarithmic distribution of signals and the sparsity offer a new challenge.\n", "\n", "The SN-GAN is a nice alternative to the Wasserstein GAN, by \"encoding\" the Lipschitz constrain directly in the weights of\n", "the discriminator.\n", "For this reason, let us implement our first SN-GAN." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementation of SN-GAN\n", "\n", "No we are going to implement a Generative Adversarial Network using spectral normalization in the disciriminator.\n", "\n", "First of all we need the basic imports:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import tutorial as tut\n", "\n", "layers = tf.layers\n", "tfgan = tf.contrib.gan" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [], "source": [ "LATENT_DIM = 512\n", "path_cr = tut.get_file(\"gan/data/airshower_footprints.npz\")\n", "\n", "def cr_batched_dataset(BATCH_SIZE, LATENT_DIM, generator_fn):\n", " Dataset = tf.data.Dataset.from_generator(\n", " lambda: generator_fn(LATENT_DIM), output_types=(tf.float32, tf.float32),\n", " output_shapes=(tf.TensorShape((LATENT_DIM,)), tf.TensorShape((9, 9, 1))))\n", " return Dataset.batch(BATCH_SIZE)\n", "\n", "\n", "def cr_data_generator(LATENT_DIM):\n", " file = np.load(path_cr)\n", " footprints = file['shower_maps']\n", " nsamples = footprints.shape[0]\n", " while True:\n", " noise = np.random.randn(nsamples, LATENT_DIM)\n", " idx = np.random.permutation(nsamples)\n", " for i in idx:\n", " yield (noise[i], footprints[i])" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "Let us check the pipeline and plot a few air shower footprints from the \"real dataset\".\n", "The footprints were already preprocessed using $S' = log10(S+1)\\;$ and by shifting the hottest station into the center of \n", "the footprint." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from plotting import plot_multiple_footprints\n", "import itertools\n", "\n", "test_footprints = np.array(list(itertools.islice(cr_data_generator(0), 16)))[:, 1]\n", "plot_multiple_footprints(test_footprints)\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "\n", "To generate air shower footprints we now can start to implement our generator and discriminator.\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [], "source": [ "LATENT_DIM=80\n", "\n", "def generator_fn_cr(x, latent_dim=LATENT_DIM):\n", " x = layers.Dense(1 * 1 * 256, activation='relu', input_shape=(latent_dim,))(x) #\n", " x = tf.reshape(x, shape=[BATCH_SIZE, 1, 1, 256])\n", " x = layers.Conv2DTranspose(256, (3, 3), strides=(3, 3), padding='same', activation='relu')(x)\n", " x = layers.BatchNormalization()(x)\n", " x = layers.Conv2DTranspose(128, (3, 3), strides=(3, 3), padding='same', activation='relu')(x)\n", " x = layers.BatchNormalization()(x)\n", " x = layers.Conv2DTranspose(64, (3, 3), padding='same', activation='relu')(x)\n", " x = layers.BatchNormalization()(x)\n", " x = layers.Conv2D(64, (3, 3), padding='same', activation='relu')(x)\n", " x = layers.BatchNormalization()(x)\n", " x = layers.Conv2D(1, (3, 3), padding='same', kernel_initializer=tf.initializers.random_uniform(minval=0, maxval=2.), activation='relu')(x)\n", " return x\n", " " ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false } }, "source": [ "This generator just follows the typical DCGAN structure using transposed convolutions and maps from `(laten_dim,)` to footprints\n", "with shape `(9,9,1)`.\n", "\n", "Note that in the last layer we need to have a 'ReLU' activation, as the footprints are very sparse.\n", "In the early stage of the training, this can lead to dying ReLUs and sparse gradients, hence we can initialize the\n", "bias vector to 0 (default) and the weights between [0, 2] to overcome this issue.\n", "\n", "Thereafter, we can build our discriminator. Here, we need to implement spectral normalization in our layers.\n", "\n", "### Implementation of the Spectral norm\n", "The implementation of the spectral norm is relatively straight forward, we just have to control the weights in the respective layer.\n", "Hence, we have to wrap the normalization around the normal layer.\n", "\n", "Let us try to build a fully connected layer using spectral normalization." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "pycharm": { "is_executing": false, "metadata": false, "name": "#%%\n" } }, "outputs": [], "source": [ "from ganlayers import spectral_norm # function returns W / SN(W), normalized weight matrix using the power iteration method\n", "\n", "def dense_sn(inputs, units, name,\n", " activation_fn=None,\n", " use_bias=True,\n", " kernel_initializer=tf.glorot_uniform_initializer(),\n", " bias_initializer=tf.zeros_initializer(),\n", " use_gamma=False,\n", " factor=None):\n", "\n", " input_shape = inputs.get_shape().as_list()\n", "\n", " with tf.variable_scope(name):\n", " kernel = tf.get_variable('kernel', shape=(input_shape[-1], units), initializer=kernel_initializer) # create new weight matrix\n", " outputs = tf.matmul(inputs, spectral_norm(kernel, use_gamma=use_gamma, factor=factor)) # apply linear transformation\n", " if use_bias is True:\n", " bias = tf.get_variable('bias', shape=(units,), initializer=bias_initializer)\n", " outputs = tf.nn.bias_add(outputs, bias) # add bias\n", " if activation_fn is not None:\n", " outputs = activation_fn(outputs) # apply non-linear activation to the output \n", "\n", " return outputs\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false } }, "source": [ "\n", "We can now use the layer to build the discriminator. (The layer `conv2d_sn` is implemented in a similar way as `dense_sn`.)\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "pycharm": { "metadata": false, "name": "#%%\n" } }, "outputs": [], "source": [ "from ganlayers import conv2d_sn\n", "\n", "def discriminator_fn_cr(x):\n", " x = conv2d_sn(x, 64, (3, 3), name='sn_conv00', padding='same')\n", " x = tf.nn.leaky_relu(x, 0.2)\n", " x = layers.BatchNormalization()(x)\n", " x = conv2d_sn(x, 64, (4, 4), name='sn_conv01', padding='same', strides=(2, 2))\n", " x = tf.nn.leaky_relu(x, 0.2)\n", " x = layers.BatchNormalization()(x)\n", " x = conv2d_sn(x, 128, (3, 3), name='sn_conv10', padding='same')\n", " x = tf.nn.leaky_relu(x, 0.2)\n", " x = layers.BatchNormalization()(x)\n", " x = conv2d_sn(x, 128, (4, 4), name='sn_conv11', padding='same', strides=(2, 2))\n", " x = tf.nn.leaky_relu(x, 0.2)\n", " x = layers.BatchNormalization()(x)\n", " x = conv2d_sn(x, 256, (3, 3), name='sn_conv20', padding='same')\n", " x = tf.nn.leaky_relu(x, 0.2)\n", " x = layers.Flatten()(x)\n", " x = dense_sn(x, 1, name='output')\n", " return x\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "pycharm": {} }, "outputs": [], "source": [ "BATCH_SIZE = 128 # number of samples fed into the framework in each iteration\n", "GEN_LR = 0.0001 # learning rate of the generator\n", "DIS_LR = 0.0001 # learning rate of the discriminator\n", "GP = 10 # factor to scale the gradient penalty (higher means larger enforcing the Lipschitz constrain)\n", "N_CRIT = 1 # number of critic iterations per generator iterations." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "After the last layer we do not need to apply an activation, hence this can be controlled using the loss provided by `tf.gan`.\n", "\n", "Subsequently, we can build our estimator.\n", "Remember that we need the [**non saturating** loss](#ns_gan) `tfgan.losses.modified_generator_loss`, to prevent vanishing gradients.\n" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "pycharm": { "metadata": false, "name": "#%% \n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Using config: {'_save_checkpoints_secs': None, '_num_ps_replicas': 0, '_keep_checkpoint_max': 3, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': , '_model_dir': '/home/jonas/PycharmProjects/iml2019/data/gan/models/SN_GAN', '_protocol': None, '_save_checkpoints_steps': 10000, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_session_config': allow_soft_placement: true\n", "graph_options {\n", " rewrite_options {\n", " meta_optimizer_iterations: ONE\n", " }\n", "}\n", ", '_tf_random_seed': None, '_save_summary_steps': 100, '_device_fn': None, '_experimental_distribute': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_evaluation_master': '', '_eval_distribute': None, '_train_distribute': None, '_master': ''}\n" ] } ], "source": [ "sn_dir = tut.get_file(\"gan/models/SN_GAN\", is_dir=True)\n", "\n", "gan_estimator = tfgan.estimator.GANEstimator(\n", " sn_dir,\n", " generator_fn=generator_fn_cr,\n", " discriminator_fn=discriminator_fn_cr,\n", " generator_loss_fn=tfgan.losses.modified_generator_loss,\n", " discriminator_loss_fn=tfgan.losses.modified_discriminator_loss,\n", " generator_optimizer=tf.train.AdamOptimizer(GEN_LR, 0.5),\n", " discriminator_optimizer=tf.train.AdamOptimizer(DIS_LR, 0.5),\n", " get_hooks_fn=tfgan.get_sequential_train_hooks(tfgan.GANTrainSteps(1, N_CRIT)),\n", " config=tf.estimator.RunConfig(save_summary_steps=100, keep_checkpoint_max=3, save_checkpoints_steps=10000),\n", " use_loss_summaries=True)\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "Afterwards, we could train our estimator.\n", "\n", "### Results\n", "Instead of training, we again load a pretrained model which was trained around 5h on a Nvidia 1080 GTX and plot some \n", "air shower footprints generated by our model." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "pycharm": { "metadata": false, "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Skipping training since max_steps has already saved.\n", "INFO:tensorflow:Calling model_fn.\n", "INFO:tensorflow:Done calling model_fn.\n", "INFO:tensorflow:Graph was finalized.\n", "INFO:tensorflow:Restoring parameters from /home/jonas/PycharmProjects/iml2019/data/gan/models/SN_GAN/model.ckpt-305000\n", "INFO:tensorflow:Running local_init_op.\n", "INFO:tensorflow:Done running local_init_op.\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "gan_estimator.train(lambda: cr_batched_dataset(BATCH_SIZE, LATENT_DIM, cr_data_generator), max_steps=1000)\n", "iter = gan_estimator.predict(lambda: cr_batched_dataset(BATCH_SIZE, LATENT_DIM, cr_data_generator))\n", "generated = []\n", "\n", "for i, gen_foot in zip(range(1000), iter):\n", " generated.append(gen_foot)\n", "\n", "generated = np.array(generated) \n", "plot_multiple_footprints(generated)\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "We can see concentrated showers in the center of the array cutout, and a decrease of signals for stations with\n", "higher distances to the shower core. This is exactly what we would expect from physics.\n", "\n", "Let us now do short but more quantitative analysis of our generated showers and compare the distributions of total signals." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "pycharm": { "metadata": false, "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from plotting import plot_total_signal\n", "import tutorial as tut\n", "\n", "path_cr = tut.get_file(\"gan/data/airshower_footprints.npz\")\n", "file = np.load(path_cr)\n", "data = file['shower_maps'][0:1000]\n", "\n", "plot_total_signal(fake=np.sum(generated, axis=(3, 2, 1)), data=np.sum(\n", " data, axis=(3, 2, 1)))\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "Furthermore, we can investigate the sparsity of the images and compare if the distributions of stations with measured signals \n", "is similar." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "pycharm": { "metadata": false, "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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ELGCWpB9GxPkdGBMkce1LMkPfG8CdkhZExJ1lMU4FpgLU1NREB8doZtZtZD7B3YZEsRLYsWS9f1rWZJ20n2JLko7uFcCfIuKliHgDqANGbGQcZmbWRnmHKN8Y9cAQSYMkbQJM5P15vBvVApPS5WOAu9JxqOYCn5bUJ00i/8IH+zrMzKwD5bl1dqOkfRCnknzw9wCuj4hFks4DGiKiFpgG3ChpCfAKSUIhIl6VdAlJwgmgLiJuKypWMzNrWa5kIWlfYEhE/EpSX2CziHgua7+IqCO5hFRadk7J8lvAsc3sexOeN8PMrCJ48iMzM8vkyY/MzCyTJz8yM7NMnvzIzMwyefIjMzPLlJksJH0H+J0ThJlZ95XnMtTmwB2S7pN0qqSPFx2UmZlVljzDffwoIj4JnALsANwraX7hkZmZWcVozXAfLwL/IBm7aftiwjEzs0qU56G8r0u6B7iTZPjwkyJij6IDMzOzypFnuI8dgW9HxMKigzEzs8rU0rSqW0TE68DP0vVtSrdHxCsFx2ZmZhWipZbFb4AjgQUkT2+XTqsawM4FxmVmZhWkpZnyjkz/HdRx4ZiZWSXK81DenRFxcFaZdX2Xznu6yfLTDtm1gyMxs47WUp9Fb6APsJ2krXn/MtQWQL8OiM0qzJjlU5vZcnGHxmFmHa+llsXXgG8DnyDpt2hMFq8DVxYcl5mZVZCW+iwuAy6T9I2IuKIDYzIzswqTZ9TZKyR9ChgK9C4p/3WRgZmZWeXI08F9LnAASbKoAw4H/gdwsjAz6ybyjA11DHAw8I+I+AowDNiy0KjMzKyi5EkWb0bEBmC9pC1IBhTcsdiwzMyskuQZG6pB0lYkU6kuANYCDxQalVU9P5Nh1rXk6eD+erp4jaTbgS0i4rFiwzIzs0rS0kN5I1raFhGPFBOSdQV+gM+sa2mpZfFfLWwL4KB2jsXMzCpUSw/lHdjWg0saC1wG9ACui4iflG3flOQW3L1IZuCbEBHLSrYPAJ4EpkSEv5KamXWSPDPl9ZF0tqSp6foQSUfm2K8HcBXJcxlDgS9JGlpW7UTg1YgYDFwKXFS2/RLgj9mnYWZmRcpz6+yvgHeAfdL1lcAFOfYbBSyJiKUR8Q4wExhfVmc8cEO6PAs4WJIAJB0FPAcsyvFaZmZWoDzJYpeI+CmwDiAi3uCDEyE1px/wfMn6Cj48Wu17dSJiPbAa2FbSZsAPgB+19AKSJktqkNSwatWqHCGZmdnGyJMs3pH0UZJObSTtArxdaFQwBbg0Ita2VCkipkZETUTU9O3bt+CQzMy6rzwP5Z0L3A7sKGkG8BnghBz7reSDT3r3T8uaqrNCUk+SYUReBkYDx0j6KbAVsEHSWxHhodEr0d0/7uwIzKxgLSaLtP/gr8AXgDEkl5++FREv5Th2PTBE0iCSpDAROK6sTi0wieSJ8GOAuyIigP1KYpgCrHWiMDPrPC0mi4gISXUR8WngttYcOCLWSzoVmEty6+z1EbFI0nlAQ0TUAtOAGyUtAV4hSShWZR5Y+nJnh2BmBctzGeoRSSMjor61B4+IOpJhzUvLzilZfgs4NuMYU1r7umZm1r7yJIvRwL9J+hvwT5JLURERexQamZmZVYw8yeKwwqMwM7OKlmfU2b91RCBmZla58rQsrA2am9dhTAfHYWbWFnkeyjMzs27OycLMzDL5MpR1qKYuy3mqVbPK55aFmZllcrIwM7NMThZmZpbJycLMzDI5WZiZWSYnCzMzy+RbZ63TNfeUu2+pNascblmYmVkmtyys041ZPrWZLRd3aBxm1jy3LMzMLJOThZmZZXKyMDOzTO6zsA7VfP+EmVUytyzMzCyTk4WZmWXyZSirPnf/+MNlB57Z8XGYdSNuWZiZWSa3LArmDl0z6woKbVlIGivpKUlLJJ3RxPZNJf0u3f6QpIFp+SGSFkh6PP33oCLjNDOzlhXWspDUA7gKOARYAdRLqo2IJ0uqnQi8GhGDJU0ELgImAC8Bn4uIFyR9CpgL9CsqVqtMzQ4w6PawWYcrsmUxClgSEUsj4h1gJjC+rM544IZ0eRZwsCRFxF8i4oW0fBHwUUmbFhirmZm1oMhk0Q94vmR9BR9uHbxXJyLWA6uBbcvqHA08EhFvFxSnmZllqOgGvaRPklyaOrSZ7ZOByQADBgzowMjMzLqXIlsWK4EdS9b7p2VN1pHUE9gSeDld7w/MBo6PiGebeoGImBoRNRFR07dv33YO38zMGhWZLOqBIZIGSdoEmAjUltWpBSaly8cAd0VESNoKuA04IyLuLzBGMzPLobDLUBGxXtKpJHcy9QCuj4hFks4DGiKiFpgG3ChpCfAKSUIBOBUYDJwj6Zy07NCIeLGoeK3yNPuMys7l3VpmVrRC+ywiog6oKys7p2T5LeDYJva7ALigyNjMzCw/D/dhZmaZnCzMzCyTk4WZmWVysjAzs0xOFmZmlqmin+A2y62pCZHAkyKZtRO3LMzMLJOThZmZZXKyMDOzTO6zsKrzwNKXP1S2t4cAMSuUWxZmZpbJycLMzDI5WZiZWSYnCzMzy+QObuuemnqIzw/wmTXLLQszM8vkloV1bc0NA2JmreKWhZmZZXLLwrqEph7UAz+sZ9ZenCzMsnhEWzNfhjIzs2xuWViX5stTZu3DLQszM8vkloV1S02OXItvszVrjlsWZmaWyS0Ls43VmiFDPLyIVblCk4WkscBlQA/guoj4Sdn2TYFfA3sBLwMTImJZuu1M4ETgXeCbETG3yFjbhZ8WNrMuqrDLUJJ6AFcBhwNDgS9JGlpW7UTg1YgYDFwKXJTuOxSYCHwSGAtcnR7PzMw6QZEti1HAkohYCiBpJjAeeLKkznhgSro8C7hSktLymRHxNvCcpCXp8R4oMF7r5trlNtt2aF0+MO30/HH4UpZ1kCI7uPsBz5esr0jLmqwTEeuB1cC2Ofc1M7MOUtUd3JImA5PT1bWSnmrD4bYDXmp7VBXL51dxzmrtDk2cY6uPUcmq8G/YKpV6fjvlqVRkslgJ7Fiy3j8ta6rOCkk9gS1JOrrz7EtETAWmtkewkhoioqY9jlWJfH7Vr6ufo8+vshV5GaoeGCJpkKRNSDqsa8vq1AKT0uVjgLsiItLyiZI2lTQIGAI8XGCsZmbWgsJaFhGxXtKpwFySW2evj4hFks4DGiKiFpgG3Jh2YL9CklBI691M0hm+HjglIt4tKlYzM2tZoX0WEVEH1JWVnVOy/BZwbDP7XghcWGR8ZdrlclYF8/lVv65+jj6/Cqbkqo+ZmVnzPDaUmZll6vbJQtJYSU9JWiLpjM6Opz1Iul7Si5KeKCnbRtI8Sc+k/27dmTG2haQdJd0t6UlJiyR9Ky3vEucoqbekhyU9mp7fj9LyQZIeSt+rv0tvHKlaknpI+oukP6TrXe38lkl6XNJCSQ1pWdW+R7t1ssg5JEk1mk4yTEqpM4A7I2IIcGe6Xq3WA9+NiKHAGOCU9O/WVc7xbeCgiBgGDAfGShpDMhzOpenwOK+SDJdTzb4FLC5Z72rnB3BgRAwvuWW2at+j3TpZUDIkSUS8AzQOSVLVIuJPJHeXlRoP3JAu3wAc1aFBtaOI+HtEPJIuryH5wOlHFznHSKxNV3ulPwEcRDIsDlTx+QFI6g8cAVyXrosudH4tqNr3aHdPFt1pWJGPR8Tf0+V/AB/vzGDai6SBwJ7AQ3Shc0wv0SwEXgTmAc8Cr6XD4kD1v1d/Dnwf2JCub0vXOj9IEvwdkhako01AFb9Hq3q4D9s4ERGSqv42OEmbAbcA346I15Mvp4lqP8f0uaLhkrYCZgO7dXJI7UbSkcCLEbFA0gGdHU+B9o2IlZK2B+ZJ+mvpxmp7j3b3lkWuYUW6iP+VtANA+u+LnRxPm0jqRZIoZkTE79PiLnWOABHxGnA3sDewVTosDlT3e/UzwDhJy0gu/R5EMu9NVzk/ACJiZfrviyQJfxRV/B7t7skiz5AkXUXp0CqTgP/uxFjaJL2+PQ1YHBGXlGzqEucoqW/aokDSR4FDSPpl7iYZFgeq+Pwi4syI6B8RA0n+z90VEf9GFzk/AEkfk7R54zJwKPAEVfwe7fYP5Un6V5Lrp41DknTkU+OFkPRb4ACSUS7/FzgXmAPcDAwA/gZ8MSLKO8GrgqR9gfuAx3n/mvdZJP0WVX+OkvYg6fzsQfKF7uaIOE/SziTfxLcB/gL8ezrnS9VKL0OdHhFHdqXzS89ldrraE/hNRFwoaVuq9D3a7ZOFmZll6+6XoczMLAcnCzMzy+RkYWZmmZwszMwsk5OFmZllcrKwqiPpHkmFz2Us6ZuSFkua0cbjTJd0TLrcbrFLGtc4UrKko0oHwWzL60iqa3zOo71IGlg6CrJVHw/3Yd2KpJ4l4w9l+Trw2YhYUWRMGyudmrjxIdKjgD+QTEXc1uP+a1uPYV2PWxZWiPSb5GJJ16ZzMtyRPo38gW+9krZLh31A0gmS5qTj/C+TdKqk76RzHjwoaZuSl/hyOk/AE5JGpft/TMlcHg+n+4wvOW6tpLtIhoUuj/U76XGekPTttOwaYGfgj5JOK6vfQ9LFaf3HJH0jLd9L0r3pwHFzG4d1aOb30yNtcTyhZM6Dpl7jOSW2kvSupP3TbX+SNCQ9rysl7QOMA36W/k52SQ9zbPq7eFrSfk3EsEN6rMbf435p+TJJ26XLP1Qy38v/SPqtpNNL/oYXlR8//bvfJ+mR9Gef5n4HVl3csrAiDQG+FBEnSboZOBq4KWOfT5GMItsbWAL8ICL2lHQpcDzJ0/YAfSJiePoBen2633+SDB3x1fQyysOS5qf1RwB7lD8tK2kv4CvAaEDAQ5LujYiTJY0lmY/gpbIYJwMDgeERsV7JhDa9gCuA8RGxStIEkjnkv9rMeQ4H+kXEp9I4PnDZJyLelfQUyTwrg4BHgP0kPQTsGBHPSPpMWvfPkmqBP0TErPR4AD0jYpSSUQrOBT5bFsNxwNz0yeIeQJ+y381Ikr/ZMJJh0h8BFpRUaer4LwKHRMRbkoYAvwUKv2RoxXOysCI9FxEL0+UFJB+wWe5O56hYI2k1cGta/jiwR0m930Iyd4ekLdIP20NJBqg7Pa3Tm2RYBYB5zQyrsC8wOyL+CSDp98B+JMNNNOezwDWNl7Mi4hVJnyJJWPPSD+oewN+bPwRLgZ0lXQHcBtzRRJ37gP1JksWPgZOAe0nGNMujcYDF5n739cD1aaKbU/K3avQZ4L8j4i3gLUm3lm1v6vi9gCslDQfeBXbNGatVOF+GsiKVjuvzLu9/OVnP+++93i3ss6FkfQMf/HJTPk5NkLQMjk5nJhseEQMionEmtn9uRPytIWBRyWt/OiIOba5yRLxK8o39HuBk0kmAyvyJJHGNAuqArUjG/LovZ0yNv7vS331pDH8iSUYrgemSjs953JaOfxrJeGTDSFoUVT01qr3PycI6wzJgr3T5mBbqtWQCvDeo4OqIWA3MBb6h9Ku9pD1zHOc+4ChJfZSMDvp5sj+M5wFfUzqcdtqX8hTQV9LeaVkvSZ9s7gBpn8BHIuIW4GySy2TlHgb2ATak3+4XAl8jSSLl1gCbZ8RdHsNOwP9GxLUkyao8hvuBzymZE3wz4Mgch90S+HtEbAC+TNLCsi7AycI6w8XAf0j6C8nIuBvjrXT/a3h/rubzSS6DPCZpUbreonR61ukkH8wPAddFREuXoCD5YF2evs6jwHHptLzHABelZQtJPuib0w+4R8lseDcBZzYR29skMzk+mBbdR5IQHm/ieDOB76Ud+7s0sb0pBwCPpr/HCSRzSpS+fj3J3VaPAX9MX3d1xjGvBialv4PdKL5FZx3Eo86aWbMkbRYRayX1IWnRTG6c/9y6F3dwm1lLpip52K83cIMTRfflloWZmWVyn4WZmWVysjAzs0xOFmZmlsnJwszMMjlZmJlZJicLMzPL9P8BaZxBSURGBOMAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from plotting import plot_cell_number_histo\n", "\n", "plot_cell_number_histo(fake=np.sum(generated > 0, axis=(\n", " 3, 2, 1)), data=np.sum(data > 0, axis=(3, 2, 1)))\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "###### Discriminator Loss \n", "When studying the loss of the discriminator we can not see any kind of convergence.\n", "We have still the same issue of not estimating a distance (something like \"relative differences\") in the discriminator but classifying only.\n", "\"drawing\"\n", "\n", "###### Generated samples during training\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "      \n", "# Generating Calorimeter Images\n", "\n", "Let us now put all together and build a combination of spectral normalization and Wasserstein\n", "The _gradient penalty_ put a similar constrain on the critic/discriminator, but in a different way then spectral normalization.\n", "\n", "##### Differences between Spectral Normalization and Gradient Penalty\n", "| Wasserstein | Spectral Normalization |\n", "| :-------------: | :-------------: |\n", "| constrains gradients directly | uses weight normalization |\n", "| non stationary training routine \"local normalization\" | relatively stable training \"global normalization\" |\n", "| $\\checkmark$ nearly no constrain of feature learning | $\\times$ can reduce complexity of feature space |\n", "| $\\times$ needs $\\geq 5$ discriminator iterations | $\\checkmark$ only $1$ discriminator iteration needed |\n", "| $\\times$ needs tuning of learning rates and \"ncr\" iterations | $\\checkmark$ stable training for a wide range of parameters |\n", "| $\\checkmark$ provides meaningful metric | $\\times$ does not provide meaningful metric |\n", "\n", " So let us take the best of both worlds and combine both techniques. \n", "\n", "### Spectral Normalization in the generator\n", "\n", "Recently, it was found that the singular values of the generator are related to the GAN performance.\n", "[Odena et al.](https://arxiv.org/abs/1802.08768) proposed to use __Jacobian clamping__ to stabilize the training process.\n", "Since, spectral normalization is performing similar we will use spectral normalization instead.\n", "\n", "### Implementation\n", "Let us use for this last GAN implementation a much more complicated dataset as very similar used in [Erdmann, Glombitza, Quast](https://link.springer.com/article/10.1007%2Fs41781-018-0019-7).\n", "For simplicity reasons we are only using 3 layers and a single energy of 100 GeV electrons.\n", "Hence, using label conditioning is useless in this setup. \n", "\n", "So let us begin to implement the data pipeline and plot some calorimeter images." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "pycharm": { "metadata": false, "name": "#%%\n" } }, "outputs": [ { "data": { 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "from plotting import plot_calo_images\n", "import tutorial as tut\n", "\n", "path_calo = tut.get_file(\"gan/data/3_layer_calorimeter_padded.npz\")\n", "LATENT_DIM = 64\n", "\n", "def calo_batched_dataset(BATCH_SIZE, LATENT_DIM, generator_fn):\n", " Dataset = tf.data.Dataset.from_generator(\n", " lambda: generator_fn(LATENT_DIM), output_types=(tf.float32, tf.float32),\n", " output_shapes=(tf.TensorShape((LATENT_DIM,)), tf.TensorShape((15, 15, 3))))\n", " return Dataset.batch(BATCH_SIZE)\n", "\n", "\n", "def calo_data_generator(LATENT_DIM):\n", " calo_ims = np.load(path_calo)['data']\n", " nsamples = calo_ims.shape[0]\n", " calo_ims = np.log10(calo_ims+1)\n", " calo_ims = calo_ims.astype(np.float32) \n", " while True:\n", " noise = np.random.randn(nsamples, LATENT_DIM)\n", " idx = np.random.permutation(nsamples)\n", " for i in idx:\n", " yield (noise[i], calo_ims[i])\n", "\n", "\n", "import itertools\n", "images = np.array(list(itertools.islice(calo_data_generator(1), 3)))[:, 1]\n", "images = 10**images - 1\n", "plot_calo_images(images)\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "We can observed sparse showers evolving in the electromagnetic sampling calorimeter.\n", "Furthermore, we can see that our calorimeter features a hexagonal grid. Therefore, we use offset coordinates as described\n", "in [Astropart. Phys. 97 (2017) 46](https://www.sciencedirect.com/science/article/pii/S0927650517302219)." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "pycharm": { "metadata": false, "name": "#%%\n" } }, "outputs": [], "source": [ "layers = tf.layers\n", "tfgan = tf.contrib.gan\n", "from ganlayers import conv2d_transpose_sn, conv2d_sn, dense_sn\n", "\n", "def generator_fn(x):\n", " x = layers.Dense(3 * 3 * 256, activation='relu')(x) #\n", " x = tf.reshape(x, shape=[BATCH_SIZE, 3, 3, 256])\n", " x = conv2d_transpose_sn(x, 256, (4, 4), strides=(2, 2), padding='same', name='sn_conv_gen_transposed01', activation=tf.nn.relu)\n", " x = layers.BatchNormalization()(x)\n", " x = conv2d_transpose_sn(x, 128, (3, 3), strides=(2, 2), padding='valid', name='sn_conv_gen_transposed02', activation=tf.nn.relu)\n", " x = layers.BatchNormalization()(x)\n", " x = conv2d_transpose_sn(x, 64, (3, 3), padding='valid', name='sn_conv_gen_transposed03', activation=tf.nn.relu)\n", " x = layers.BatchNormalization()(x)\n", " x = conv2d_sn(x, 64, (3, 3), name=\"sn_gen_conv01\", padding='same', activation=tf.nn.relu)\n", " x = layers.BatchNormalization()(x)\n", " x = conv2d_sn(x, 3, (3, 3), name=\"sn_gen_conv02\", padding='same', kernel_initializer=tf.initializers.random_uniform(minval=0.1, maxval=100.), activation=tf.nn.relu)\n", " return x\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "In the generator we make use of spectral normalization and batch normalization.\n", "Note, that we again uses the initialization trick in the last layer of the generator to overcome dying ReLUs and sparse gradients which can lead to a collapse of the training.." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "pycharm": { "metadata": false, "name": "#%%\n" } }, "outputs": [], "source": [ "def discriminator_fn(x):\n", " \"\"\" Discriminator network \"\"\"\n", " x = conv2d_sn(x, 64, (3, 3), name=\"sn_conv01\", padding='same')\n", " x = tf.contrib.layers.layer_norm(x)\n", " x = tf.nn.leaky_relu(x, 0.2)\n", " x = conv2d_sn(x, 64, (5, 5), name=\"sn_conv02\", padding='same', strides=(2, 2))\n", " x = tf.contrib.layers.layer_norm(x)\n", " x = tf.nn.leaky_relu(x, 0.2)\n", " x = conv2d_sn(x, 128, (3, 3), name=\"sn_conv03\", padding='same')\n", " x = tf.contrib.layers.layer_norm(x)\n", " x = tf.nn.leaky_relu(x, 0.2)\n", " x = conv2d_sn(x, 128, (4, 4), name=\"sn_conv04\", padding='same', strides=(2, 2))\n", " x = tf.contrib.layers.layer_norm(x)\n", " x = tf.nn.leaky_relu(x, 0.2)\n", " x = conv2d_sn(x, 256, (3, 3), name=\"sn_conv05\", padding='same')\n", " x = tf.contrib.layers.layer_norm(x)\n", " x = tf.nn.leaky_relu(x, 0.2)\n", " x = conv2d_sn(x, 512, (3, 3), name=\"sn_conv06\", padding='same')\n", " x = tf.contrib.layers.layer_norm(x)\n", " x = tf.nn.leaky_relu(x, 0.2)\n", " x = layers.Flatten()(x)\n", " x = dense_sn(x, 1, name='sn_dense01')\n", " return x\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false } }, "source": [ "After we successfully implemented our generator and critic network, we need to implement the Wasserstein loss with gradient penalty\n" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "pycharm": { "metadata": false, "name": "#%%\n" } }, "outputs": [], "source": [ "def discriminator_loss(model, add_summaries=True):\n", "\n", " loss = tf.contrib.gan.losses.wasserstein_discriminator_loss(model, add_summaries=add_summaries)\n", " gp_loss = GP * tf.contrib.gan.losses.wasserstein_gradient_penalty(model, epsilon=1e-10, one_sided=True, add_summaries=add_summaries)\n", " loss += gp_loss\n", "\n", " if add_summaries:\n", " tf.summary.scalar('discriminator_loss', loss)\n", "\n", " return loss\n", " " ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "To start generating calorimeter samples let us build our framework using the GANEstimator and set the training parameters." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "pycharm": { "metadata": false, "name": "#%% \n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Using config: {'_save_checkpoints_secs': None, '_num_ps_replicas': 0, '_keep_checkpoint_max': 3, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': , '_model_dir': '/home/jonas/PycharmProjects/iml2019/data/gan/models/WGAN_SN', '_protocol': None, '_save_checkpoints_steps': 10000, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_session_config': allow_soft_placement: true\n", "graph_options {\n", " rewrite_options {\n", " meta_optimizer_iterations: ONE\n", " }\n", "}\n", ", '_tf_random_seed': None, '_save_summary_steps': 100, '_device_fn': None, '_experimental_distribute': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_evaluation_master': '', '_eval_distribute': None, '_train_distribute': None, '_master': ''}\n" ] } ], "source": [ "BATCH_SIZE = 64 # number of samples fed into the framework in each iteration\n", "GEN_LR = 0.0001 # learning rate of the generator\n", "DIS_LR = 0.0001 # learning rate of the discriminator\n", "GP = 10 # factor to scale the gradient penalty (higher means larger enforcing the Lipschitz constrain)\n", "N_CRIT = 5 # number of critic iterations per generator iterations.\n", "\n", "wgan_sn_dir = tut.get_file(\"gan/models/WGAN_SN\", is_dir=True)\n", "\n", "gan_estimator = tfgan.estimator.GANEstimator(\n", " wgan_sn_dir,\n", " generator_fn=generator_fn,\n", " discriminator_fn=discriminator_fn,\n", " generator_loss_fn=tfgan.losses.wasserstein_generator_loss,\n", " discriminator_loss_fn=discriminator_loss,\n", " generator_optimizer=tf.train.AdamOptimizer(GEN_LR, 0.5, 0.9),\n", " discriminator_optimizer=tf.train.AdamOptimizer(DIS_LR, 0.5, 0.9),\n", " get_hooks_fn=tfgan.get_sequential_train_hooks(tfgan.GANTrainSteps(1, N_CRIT)),\n", " config=tf.estimator.RunConfig(save_summary_steps=100, keep_checkpoint_max=3, save_checkpoints_steps=10000),\n", " use_loss_summaries=True)\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "Again we load a pretrained model and inspect first samples generated by our model.\n" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "pycharm": { "metadata": false, "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling model_fn.\n", "INFO:tensorflow:Done calling model_fn.\n", "INFO:tensorflow:Graph was finalized.\n", "INFO:tensorflow:Restoring parameters from /home/jonas/PycharmProjects/iml2019/data/gan/models/WGAN_SN/model.ckpt-200000\n", "INFO:tensorflow:Running local_init_op.\n", "INFO:tensorflow:Done running local_init_op.\n", "('iteration', 0)\n", "('iteration', 100)\n", "('iteration', 200)\n", "('iteration', 300)\n", "('iteration', 400)\n", "('iteration', 500)\n", "('iteration', 600)\n", "('iteration', 700)\n", "('iteration', 800)\n", "('iteration', 900)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from plotting import plot_average_image, plot_calo_images\n", "\n", "calo_iter = gan_estimator.predict(lambda: calo_batched_dataset(BATCH_SIZE, LATENT_DIM, calo_data_generator))\n", "generated_calo = []\n", "for i, image in zip(range(1000), calo_iter):\n", " generated_calo.append(10**image - 1)\n", " if i%100 == 0:\n", " print(\"iteration\", i)\n", "generated_calo = np.array(generated_calo)\n", "plot_calo_images(generated_calo)\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false } }, "source": [ "We can further investigate the correlations between the layer.\n", "It is from major importance that our generative model is able to capture this correlations!\n", "\n", "Note that genrating new samples using jupyter notebooks on a CPU can be quiet time consuming. " ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "pycharm": { "metadata": false, "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from plotting import plot_layer_correlations\n", "\n", "path_calo = tut.get_file(\"gan/data/3_layer_calorimeter_padded.npz\")\n", "calo_ims = np.load(path_calo)['data'][:1000]\n", "plot_layer_correlations(np.sum(calo_ims, axis=(1, 2)), datatype='data')\n", "plot_layer_correlations(np.sum(generated_calo, axis=(1, 2)), datatype='generated')\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false } }, "source": [ "This correlation looks good for a first try 'out of the box'.\n", "\n", "Finally, we will have a look on the occupancy of the generated and real samples.\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "pycharm": { "metadata": false, "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from plotting import plot_cell_number_histo\n", "plot_cell_number_histo(fake=np.sum(generated_calo > 0, axis=(1, 2, 3)), data=np.sum(calo_ims > 0, axis=(1, 2, 3)))\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "metadata": false, "name": "#%% md\n" } }, "source": [ "The distribution of triggered cells look reasonable, not bad for a first shot!\n", "It is a well known in the HEP community (see [here](https://link.springer.com/article/10.1007%2Fs41781-018-0019-7) or [here](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.97.014021)),\n", "that the sparsity in calorimeters is a big challenge and needs further investigations.\n", "\n", "There is much more one could try:\n", "- Tune optimizer, decay learning rates, use different learning rates for generator and discriminator\n", "- Use different preprocessing\n", "- Use very deep models\n", "- Design model which captures symmetry of the problem\n", "- [Progressive growing of GANs](https://arxiv.org/abs/1710.10196)\n", "- Make use of [attention](https://arxiv.org/abs/1805.08318) (especially for high resolution cases)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }